Functional analysis of genetic risk markers

Regulatory variants are the main factors responsible for genetic predisposition to how e.g. humans react differently to the environment. Therefore, it is important to locate and measure their effects, which can result in pre-disease intervention, new drugs, or as part in the personal medicine era, where selection and dose of a drug is based on a person’s genetic profile. In this thesis we have investigated the potential to link genetic markers to transcription using allele specific expression (ASE), which can avoid influence of both population stratification bias and trans-factors, increasing the statistical power compared to using total RNA based linkage methods. To quantify expression levels, we have used RNA-sequencing, which automatically makes it possible to measure ASE, provided that there is a heterozygous variant within the transcribed fragment, which in turn makes it possible to discern the expression between the two alleles. RNA sequencing data tend to be complex and requires to be summarized into count measures before further analyzed for ASE. To facilitate this process and provide additional analytical support, we developed the software AllelicImbalance, which now is freely accessible within bioconductor, a bioinformatics repository for code and data. Using this software we investigated ASE behavior on the individual level of a single transcribed variant, within a gene, and for connections between an ASE event and known risk markers, previously established from Genome Wide Association Studies (GWAS). We showed in a dataset of 10 individuals that by measuring a consistent ASE over consecutive exons withing the same gene that an ASE signature is robust against dissimilarities in sequence. Further, because we showed that ASE stability covered several SNPs we established that short read sequencing is not a fundamental obstacle to the implementation of this technique. However, more individuals were needed to better assess a link to genetic variants. We continued our analysis in a larger dataset, in which one of the sequenced tissues had a representation of 680 individuals. This was enough to measure ASE as a regression of allelic fraction by genotype (aeQTL), conceptually similar to the regression of expression by genotype commonly used in eQTL studies. In this data we were able to explain novel risk SNPs using the aeQTL method, and showed that any bias for the reference allele had no significant effect on the regression. We moved on to test if aeQTL could pick up unique signals for 205 individuals in a tissue previously investigated for eQTL using a large cohort of more than 5000 individuals. Indeed, we detected 15 novel aeQTLs, which probably were masked by trans-regulation in the previous investigation. In addition, we describe the software ClusterSignificance, which tests for separation of groups in data with reduced dimensionality. The algorithm sets statistical rigor to a task previously done by visual inspection. This thesis gives an overview of progress of us and others in ASE investigations, which is becoming more than being just a compliment to eQTL. The future signals a more dominant role as more sequencing data becomes readily available, accessing the closest active link to cis-regulation

Genetic loci on chromosome 5 are associated with circulating levels of interleukin-5 and eosinophil count in a European population with high risk for cardiovascular disease

IL-5 is a Th2 cytokine which activates eosinophils and is suggested to have an atheroprotective role. Genetic variants in the IL5 locus have been associated with increased risk of CAD and ischemic stroke. In this study we aimed to identify genetic variants associated with IL-5 concentrations and apply a Mendelian randomisation approach to assess IL-5 levels for causal effect on intima-media thickness in a European population at high risk of coronary artery disease. We analysed SNPs within robustly associated candidate loci for immune, inflammatory, metabolic and cardiovascular traits. We identified 2 genetic loci for IL-5 levels (chromosome 5, rs56183820, BETA = 0.11, P = 6.73E−5 and chromosome 14, rs4902762, BETA = 0.12, P = 5.76E−6) and one for eosinophil count (rs72797327, BETA = −0.10, P = 1.41E−6). Both chromosome 5 loci were in the vicinity of the IL5 gene, however the association with IL-5 levels failed to replicate in a meta-analysis of 2 independent cohorts (rs56183820, BETA = 0.04, P = 0.2763, I2 = 24, I2 − P = 0.2516). No significant associations were observed between SNPs associated with IL-5 levels or eosinophil count and IMT measures. Expression quantitative trait analyses indicate effects of the IL-5 and eosinophil-associated SNPs on RAD50 mRNA expression levels (rs12652920 (r2 = 0.93 with rs56183820) BETA = −0.10, P = 8.64E−6 and rs11739623 (r2 = 0.96 with rs72797327) BETA = −0.23, P = 1.74E−29, respectively). Our data do not support a role for IL-5 levels and eosinophil count in intima-media thickness, however SNPs associated with IL-5 and eosinophils might influence stability of the atherosclerotic plaque via modulation of RAD50 levels

Genetic studies of body mass index yield new insights for obesity biology

Note: A full list of authors and affiliations appears at the end of the article. Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P 20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.</p